In conventional identification systems, an identification feature (e.g., finger print, palm-print, hand print) may be captured in a two dimensional image using a two dimensional scanner. An identification feature, such as a fingerprint may be analyzed to determine a person's identity by comparing the captured fingerprint to a database including images of fingerprints, and a person's identity may be confirmed by matching the captured fingerprint to a previously captured image of the person's fingerprint. In conventional systems, the identification feature includes a plurality of identification minutiae located at unique positions on the identification feature, where the unique locations of the identification minutiae may be identified and compared to identification minutiae on the previously captured identification feature to determine a match (i.e., determine the person's identity and/or confirm the person's identity). There are approximately 150 different types of identification minutiae for a fingerprint; however, in practice, conventional systems typically categorize the various types into two general classifications: ridge ends—for example, where a ridge of a fingerprint ends, referred to as a “termination;” and ridge splits—for example where a fingerprint ridge splits into two ridges, which is referred to as a “bifurcation.” Furthermore, for each ridge in an identification feature, a ravine (e.g., a valley) is typically proximate the ridge. Hence, for example, a fingerprint typically comprises a plurality of ridges and a plurality of proximate ravines.
Conventional systems analyze a two dimensional image of the fingerprint to identify the identification minutiae, which is generally referred to as minutiae extraction. The two dimensional image of the identification feature may be analyzed to identify identification minutiae. The extracted identification minutiae may be compared by the computer to one or more identification minutiae associated with an identity (i.e., a particular person) to determine if the captured fingerprint also corresponds to the identity. In other words, the extracted identification minutiae are used to determine a person's identity by determining if they match identification minutiae previously associated with the person.
Traditionally, identification image acquisition was based on contact. For example, a finger would be placed on a fingerprint scanner, and a two dimensional image of the fingerprint would be captured by the fingerprint scanner. In conventional two dimensional identification systems, placing the identification feature (e.g., a finger, a hand, etc.) on the two dimensional scanner introduces distortions and deformations to the captured two dimensional image. For example, pressing a finger onto a scanning surface may cause the finger to flatten and cause the ridges of the fingerprint to distort and deform in unpredictable ways. The distortions and deformations associated with two dimensional capture may lead to problems analyzing the two dimensional image to identify the identification minutiae on the two dimensional image. In turn, the errors in correctly identifying the identification minutiae may lead to incorrect identification and/or confirmation of a person's identity. In addition, pressing a finger to a surface of a scanner may cause latent fingerprint problems, where a trace of the fingerprint remains on the surface of the scanner, which may lead to forgery and hygiene problems. Other issues such as degraded or partial images caused by improper fingerprint placement, smearing, or sensor noise from a tear on a surface coating often occur in conventional two dimensional identification systems. All the issues in turn may lead to mis-identification and/or failure to determine an identity.
Three dimensional images of identification features (e.g., a finger, a hand, a palm) have been developed, such that contact is no longer required to capture the image. For example, a person may place a finger into a three dimensional scanner and a fingerprint may be captured in a three dimensional image without the person pressing the finger to a surface. As such, three dimensional images of fingerprints, palm-prints, hand-prints, and other such images may be captured without significant distortions or deformations of the identification feature being captured. Moreover, three dimensional identification image capture systems address other issues including hygiene, latent fingerprints, etc. In addition, three dimensional image capturing systems may capture large areas as images quickly. However, in conventional systems, the captured three dimensional images are typically projected into a two dimensional image, and the two dimensional image is then analyzed using conventional two dimensional methods to identify identification minutiae.
While capturing the identification feature in a three dimensional image reduces deformations and distortions associated with capturing an image of the identification feature, projecting the three dimensional image into a two dimensional image introduces deformations and distortions into the two dimensional image. As such, in these conventional systems, errors in identifying the identification minutiae, determining an identification, and/or confirming an identification may still occur.
Therefore, a significant need continues to exist in the art for improved systems and methods for identifying identification minutiae in a three dimensional image.